Annotation, data

The Annotate data set is then fed to a SAS/GRAPH procedure and gets processed along with the SAS/GRAPH code. 

CREATE LINE OF EQUIVALENCE USING AN ANNOTATE DATA SET. data annotate ... 

ANNOTATE DATA SET TO DRAW THE HORIZONTAL LINE, ESTIMATE, AND WHISKER. data annotate set odds ... 

CREATE THE ODDS RATIO PLOT. THIS IS DONE PRIMARILY THROUGH THE INFORMATION IN THE ANNOTATION DATA SET. PUT A HORIZONTAL REFERENCE LINE AT 1 WHICH IS THE LINE OF SIGNIFICANCE. proc gplot... 

In order to make the Annotate data set work well with the Y axis, the effect ... 

Numeric-symbolic approaches are particularly important in process applications because the time series of data is by far the dominant form of input data, and they are the methods of choice if annotated data exist to develop the interpretation system. With complete dependence on the annotated data to develop the feature mapping step, numeric-symbolic mappers can be used to assign labels directly. However, as the amount and coverage of available annotated data diminishes for the given label of interest, there is a need to integrate numeric-symbolic approaches with... 

Local interpretation methods encompass a wide variety of approaches that resolve decisions about input data relative to annotated data or known features that cluster. By characterizing the cluster or grouping, it is possible to use various measures to determine whether an arbitary pattern of data can be assigned the same label as the annotated grouping. All approaches are statistical, but they vary in terms of measures, which include statistical distance, variance, probability of occurrence, and pattern similarity. 

One way to impart structure to otherwise unstructured documents is to utilize a suitable markup language. The function of markup languages is to combine the text of a document with further information about the text (markup languages typically add metadata - data about data) and while metadata is normally hidden from the view of a human reader, it is available to processing software. XML allows an author to add arbitrary metadata to documents through the use of tags, which are user-defined and annotate data sources. 

The MAGE has built within it a controlled vocabulary that is used to standardize communication between data providers. However, MAGE can also be extended to encode other types of omic data beyond genomics, such as proteomic data, so long as a reference to the ontology or controlled vocabulary is provided. Although a description of the extension mechanism is beyond scope of this book, practitioners must become familiar with it in order to ensure their software products and applications will be able to accept any and all annotation data that may be submitted with the genomic data. 

The Munich Information Center for Protein Sequences (MIPS) (Mewes et al., 2000) collects and processes sequence data for the PIR-International Protein Sequence Database project. Access to the database is provided through its Web server, http //www.mips.biochem.mpg.de/. The implementation of PrIAn (Protein Input and Annotation) data input has greatly increased database entries of PIR-International. 

Microbiota-Associated Databases (Integrate Functional Annotation Data with Comparative Genome Analysis)... 

Pathways Analysis (table 7.1) contains a large set of manually curated gene and protein interactions, together with multiple viewers to display various aspects of the interactions. Both canonical pathways (i.e., generally accepted functional units) and arbitrary interaction networks can be displayed, and links to annotation data and to literature citations are accessible from the viewers. 

Structures of amphibian mucins O-Linked GP oligosaccharides Prim structures, analysis Annotation, Data collection... 

Here Y denotes a general bulk property, Tw that of pure water and Ys that of the pure co-solvent, and the y, are listed coefficients, generally up to i=3 being required. Annotated data are provided in (Marcus 2002) for the viscosity rj, relative permittivity r, refractive index (at the sodium D-line) d. excess molar Gibbs energy G, excess molar enthalpy excess molar isobaric heat capacity Cp, excess molar volume V, isobaric expansibility ap, adiabatic compressibility ks, and surface tension Y of aqueous mixtures with many co-solvents. These include methanol, ethanol, 1-propanol, 2-propanol, 2-methyl-2-propanol (tert-butanol), 1,2-ethanediol, tetrahydrofuran, 1,4-dioxane, pyridine, acetone, acetonitrile, N, N-dimethylformamide, and dimethylsulfoxide and a few others. 

Some discrepancy between the annotated data sets has been observed. Comparison of the data set created by the retrieval of DILI information from FAERS with that from the FDA-approved drug labels yielded 73% sensitivity and 73% specificity (Zhu and Kruhlak 2014), whereas the overall prediction accuracy of the in silico model to assess the drugs with DILI potential may be <70% (Chen et al., 2013b). The ambiguous annotation of drugs may substantially contribute to the limited model predictability. 

---